Improving First Call Resolution at a Leading Healthcare Payer

  • IndustryHealthcare Payer
  • Company Size 100,000+
  • Revenue $4B+
  • Location United States
01

Our Client

This Fortune 500 enterprise utilizes technology to offer comprehensive health insurance coverage nationwide. By harnessing the potential of data, they seamlessly integrate a wide array of benefits to enhance the healthcare journey for their clients.
02

Objective

The company faced challenges with low First Call Resolution (FCR) rates in one of its contact centers, leading to customers repeatedly calling back about the same issues. This not only caused frustration for customers but also drove up costs.

Leadership struggled to understand why some agents managed to resolve issues on the first call while others did not. Although their current systems documented what occurred, they failed to provide insights into the underlying reasons for these discrepancies.

 

Cost Savings
$15M
in potential savings identified by improving productivity and reducing variability  ​
Variability Reduction
40%
by creating new standards for application usage patterns to reduce process variants
03

Solution

The company collaborated with Skan AI to uncover insights aimed at enhancing their FCR rate. The implementation emphasized monitoring complete customer journeys across various applications and channels.

Skan AI analyzed the actual workflow rather than how it was intended to function. This method highlighted the precise distinctions between top-performing and average agents.

Skan AI's process intelligence platform uncovered several critical insights:

  1. Process Standardization Gaps: Skan AI found significant process variations between teams and individuals. Top performers followed specific pathways that others missed.
  2. Root Cause Patterns: The system identified the exact reasons customers needed to call back. Many stemmed from incomplete information gathering during initial calls.
  3. Application Usage Patterns: High-FCR agents used applications differently. They accessed knowledge bases more efficiently and navigated systems in a specific order.
04

Outcome

The healthcare payer achieved significant results through these insights. Skan AI's recommendations led to:

  • $4 million in savings from a 40% reduction in process variability
  • $7 million in annual savings via a 20% improvement in workforce capacity utilization
  • $4 million in savings by cutting customer account update cycle times through automation

The project identified $15 million in cost savings. More importantly, customer experience improved substantially as more issues were resolved on the first call.

 

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